In [1]:
## 設定
verbose = False
### 言語の割合の均等化
balanced = True
### LDA 用
## トピック数
n_topics = 5 # 30は多過ぎる?
## doc, term の設定
doc_type = 'form'
doc_attr = 'spell'
max_doc_size = 12
##
term_size = 'character'
term_type = 'skippy3gram'
## skippy n-gram の結合範囲
max_distance_val = round(max_doc_size * 0.8)
print(f"max_distance_val: {max_distance_val}")
## ngram を包括的にするかどうか
ngram_is_inclusive = True
### DTM 構築
## term の最低頻度
term_min_freq = 2
## 高頻度 term の濫用指標: 大きくし過ぎないように.0.05 は十分に大きい
term_abuse_threshold = 0.05
max_distance_val: 10
In [2]:
import sys, os, random, re, glob
import pandas as pd
import pprint as pp
from functools import reduce
In [3]:
## load data to process
from pathlib import Path
import pprint as pp
wd = Path(".")
##
dirs = [ x for x in wd.iterdir() if x.is_dir() and not x.match(r"plot*") ]
if verbose:
print(f"The following {len(dirs)} directories are potential targets:")
pp.pprint(dirs)
## list up files in target directory
wd = Path(".")
target_dir = "data-words" # can be changed
target_files = sorted(list(wd.glob(f"{target_dir}/*.csv")))
#
print(f"\n{target_dir} contains {len(target_files)} files to process")
pp.pprint(target_files)
data-words contains 9 files to process
[PosixPath('data-words/base-sound-English-r6e-originals.csv'),
PosixPath('data-words/base-sound-German-r1a-original.csv'),
PosixPath('data-words/base-spell-English-r6e-originals.csv'),
PosixPath('data-words/base-spell-Esperanto-r0-orginal.csv'),
PosixPath('data-words/base-spell-French-r0-originals.csv'),
PosixPath('data-words/base-spell-German-r1a-originals.csv'),
PosixPath('data-words/base-spell-Icelandic-r0-original.csv'),
PosixPath('data-words/base-spell-Russian-r0-originals.csv'),
PosixPath('data-words/base-spell-Swahili-r0-orginal.csv')]
In [4]:
import pandas as pd
## データ型の辞書
types = "spell sound freq".split(" ")
type_setting = { t : 0 for t in types }
print(type_setting)
## 言語名の辞書
langs = "english esperanto french german icelandic russian swahili".split(" ")
#langs = "english esperanto french german russian swahili".split(" ")
#langs = "english esperanto french german icelandic swahili".split(" ")
lang_setting = { lang : 0 for lang in langs }
print(lang_setting)
## 辞書と統合
settings = { 'form': None, **type_setting, **lang_setting }
print(settings)
{'spell': 0, 'sound': 0, 'freq': 0}
{'english': 0, 'esperanto': 0, 'french': 0, 'german': 0, 'icelandic': 0, 'russian': 0, 'swahili': 0}
{'form': None, 'spell': 0, 'sound': 0, 'freq': 0, 'english': 0, 'esperanto': 0, 'french': 0, 'german': 0, 'icelandic': 0, 'russian': 0, 'swahili': 0}
In [5]:
vars = list(settings.keys())
print(f"targe var names: {vars}")
d_parts = [ ]
for lang in langs:
local_settings = settings.copy()
print(f"processing: {lang}")
try:
for f in [ f for f in target_files if lang.capitalize() in str(f) ]:
print(f"reading: {f}")
# 言語名の指定
local_settings[lang] = 1
# 型名の指定
for type in vars:
if type in str(f):
local_settings[type] = 1
#
d = pd.read_csv(f, encoding='utf-8', sep = ",", on_bad_lines = 'skip') # Crucially, ...= skip
df = pd.DataFrame(d, columns = vars)
for var in [ var for var in (types + langs) if var != 'freq' ]:
df[var] = local_settings[var]
d_parts.append(df)
except IndexError:
pass
#
if verbose:
d_parts
targe var names: ['form', 'spell', 'sound', 'freq', 'english', 'esperanto', 'french', 'german', 'icelandic', 'russian', 'swahili'] processing: english reading: data-words/base-sound-English-r6e-originals.csv reading: data-words/base-spell-English-r6e-originals.csv processing: esperanto reading: data-words/base-spell-Esperanto-r0-orginal.csv processing: french reading: data-words/base-spell-French-r0-originals.csv processing: german reading: data-words/base-sound-German-r1a-original.csv reading: data-words/base-spell-German-r1a-originals.csv processing: icelandic reading: data-words/base-spell-Icelandic-r0-original.csv processing: russian reading: data-words/base-spell-Russian-r0-originals.csv processing: swahili reading: data-words/base-spell-Swahili-r0-orginal.csv
In [6]:
## データ統合
raw_df = pd.concat(d_parts)
raw_df
Out[6]:
| form | spell | sound | freq | english | esperanto | french | german | icelandic | russian | swahili | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | æbəhəlimə | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | ædmaɪə | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | ædmɪʃən | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | ædvæntɪdʒ | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | ædvaɪs | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 703 | zaidi | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 704 | ziara | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 705 | zima | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 706 | ziwa | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 707 | zoezi | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
14323 rows × 11 columns
In [7]:
## 文字数の列を追加
raw_df['size'] = [ len(x) for x in raw_df[doc_type] ]
raw_df
Out[7]:
| form | spell | sound | freq | english | esperanto | french | german | icelandic | russian | swahili | size | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | æbəhəlimə | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
| 1 | ædmaɪə | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
| 2 | ædmɪʃən | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
| 3 | ædvæntɪdʒ | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
| 4 | ædvaɪs | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 703 | zaidi | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 |
| 704 | ziara | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 |
| 705 | zima | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 |
| 706 | ziwa | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 |
| 707 | zoezi | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 |
14323 rows × 12 columns
In [8]:
## 言語名= language の列を追加
check = False
language_vals = [ ]
for i, row in raw_df.iterrows():
if check:
print(row)
for j, lang in enumerate(langs):
if check:
print(f"{i}: {lang}")
if row[lang] == 1:
language_vals.append(lang)
if verbose:
print(language_vals)
len(language_vals)
#
raw_df['language'] = language_vals
raw_df
Out[8]:
| form | spell | sound | freq | english | esperanto | french | german | icelandic | russian | swahili | size | language | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | æbəhəlimə | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | english |
| 1 | ædmaɪə | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | english |
| 2 | ædmɪʃən | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | english |
| 3 | ædvæntɪdʒ | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | english |
| 4 | ædvaɪs | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | english |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 703 | zaidi | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | swahili |
| 704 | ziara | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | swahili |
| 705 | zima | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | swahili |
| 706 | ziwa | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | swahili |
| 707 | zoezi | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | swahili |
14323 rows × 13 columns
In [9]:
## 言語の選別
select_languages = True
selected_langs = re.split(r",\s*", "english, french, german, russian, swahili")
print(f"selected languages: {selected_langs}")
if select_languages:
df_new = [ ]
for lang in selected_langs:
df_new.append(raw_df[raw_df[lang] == 1])
raw_df = pd.concat(df_new)
#
raw_df
selected languages: ['english', 'french', 'german', 'russian', 'swahili']
Out[9]:
| form | spell | sound | freq | english | esperanto | french | german | icelandic | russian | swahili | size | language | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | æbəhəlimə | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | english |
| 1 | ædmaɪə | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | english |
| 2 | ædmɪʃən | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | english |
| 3 | ædvæntɪdʒ | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | english |
| 4 | ædvaɪs | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | english |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 703 | zaidi | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | swahili |
| 704 | ziara | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | swahili |
| 705 | zima | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | swahili |
| 706 | ziwa | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | swahili |
| 707 | zoezi | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | swahili |
12773 rows × 13 columns
In [10]:
## 文字数の分布
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.hist(raw_df['size'], bins = 40)
ax.set_xlabel('length of doc')
ax.set_ylabel('freq')
plt.title(f"Length distribution for docs")
fig.show()
/var/folders/s2/lk8hdt6j10j0xyycw1lbjsm40000gn/T/ipykernel_2920/1088473461.py:12: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown fig.show()
In [11]:
## 長さで濾過
print(f"max doc size: {max_doc_size}")
original_size = len(raw_df)
raw_df = raw_df[raw_df['size'] < max_doc_size]
filtered_size = len(raw_df)
print(f"{original_size - filtered_size} cases removed")
max doc size: 12 296 cases removed
In [12]:
## 結果の検査 1
for lang in langs:
print(raw_df[lang].value_counts())
english 1 8249 0 4228 Name: count, dtype: int64 esperanto 0 12477 Name: count, dtype: int64 french 0 11492 1 985 Name: count, dtype: int64 german 0 10912 1 1565 Name: count, dtype: int64 icelandic 0 12477 Name: count, dtype: int64 russian 0 11504 1 973 Name: count, dtype: int64 swahili 0 11772 1 705 Name: count, dtype: int64
In [13]:
## 結果の検査 2
for type in types:
print(raw_df[type].value_counts())
spell 1 7588 0 4889 Name: count, dtype: int64 sound 1 9814 0 2663 Name: count, dtype: int64 freq 1 11495 1 966 1 не 1 1 то время как 1 1 северу 1 1 него 1 1 будет 1 1 образом 1 1 мышь 1 Name: count, dtype: int64
In [14]:
## 統合: 割合補正を適用
eng_reduct_factor = 0.2
if balanced:
eng_df = raw_df[raw_df['english'] == 1]
non_eng_df = raw_df[raw_df['english'] == 0]
eng_reduced_df = eng_df.sample(round(len(eng_df) * eng_reduct_factor))
raw_df = pd.concat([eng_reduced_df, non_eng_df])
raw_df
Out[14]:
| form | spell | sound | freq | english | esperanto | french | german | icelandic | russian | swahili | size | language | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 3072 | wɪp | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | english |
| 2550 | nut | 1 | 1 | 1.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | english |
| 253 | away | 1 | 1 | 1.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | english |
| 1291 | examine | 1 | 1 | 1.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | english |
| 1269 | kəntɪnyu | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | english |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 703 | zaidi | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | swahili |
| 704 | ziara | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | swahili |
| 705 | zima | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | swahili |
| 706 | ziwa | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | swahili |
| 707 | zoezi | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | swahili |
5878 rows × 13 columns
In [15]:
## 結果の検査 3
for lang in langs:
print(raw_df[lang].value_counts())
english 0 4228 1 1650 Name: count, dtype: int64 esperanto 0 5878 Name: count, dtype: int64 french 0 4893 1 985 Name: count, dtype: int64 german 0 4313 1 1565 Name: count, dtype: int64 icelandic 0 5878 Name: count, dtype: int64 russian 0 4905 1 973 Name: count, dtype: int64 swahili 0 5173 1 705 Name: count, dtype: int64
In [16]:
## 順序のランダマイズ
import sklearn.utils
raw_df = sklearn.utils.shuffle(raw_df)
In [17]:
## データ名の指定
df = raw_df[raw_df[doc_attr] == 1]
print(f"doc_attr: {doc_attr}")
df
doc_attr: spell
Out[17]:
| form | spell | sound | freq | english | esperanto | french | german | icelandic | russian | swahili | size | language | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2147 | leising | 1 | 1 | 1.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | english |
| 726 | chat | 1 | 0 | 1.0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 4 | french |
| 906 | магнит | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 6 | russian |
| 815 | cloche | 1 | 0 | 1.0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 6 | french |
| 971 | né | 1 | 0 | 1.0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | french |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4209 | woman | 1 | 1 | 1.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | english |
| 661 | vifaa | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | swahili |
| 398 | среди | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 5 | russian |
| 261 | kuhusu | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 6 | swahili |
| 593 | si | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | swahili |
4251 rows × 13 columns
In [18]:
df[df['english'] == 1]
Out[18]:
| form | spell | sound | freq | english | esperanto | french | german | icelandic | russian | swahili | size | language | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2147 | leising | 1 | 1 | 1.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | english |
| 2838 | plow | 1 | 1 | 1.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | english |
| 3375 | sew | 1 | 1 | 1.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | english |
| 2207 | lodge | 1 | 1 | 1.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | english |
| 1877 | idea | 1 | 1 | 1.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | english |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2328 | membership | 1 | 1 | 1.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | english |
| 3119 | relieve | 1 | 1 | 1.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | english |
| 2488 | neis | 1 | 1 | 1.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | english |
| 2534 | notebook | 1 | 1 | 1.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | english |
| 4209 | woman | 1 | 1 | 1.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | english |
811 rows × 13 columns
In [19]:
## ngram の追加
import sys
sys.path.append('..')
import re
import ngrams
import importlib
importlib.reload(ngrams)
import ngrams_skippy
bases = df[doc_type]
## 1gram 列の追加
#sep = r""
#unigrams = [ list(filter(lambda x: len(x) > 0, y)) for y in [ re.split(sep, z) for z in bases ] ]
unigrams = ngrams.gen_unigrams(bases, sep = r"", check = False)
if verbose:
random.sample(unigrams, 5)
#
df['1gram'] = unigrams
#df.loc[:,'1gram'] = unigrams
df
/var/folders/s2/lk8hdt6j10j0xyycw1lbjsm40000gn/T/ipykernel_2920/1248262955.py:21: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy df['1gram'] = unigrams
Out[19]:
| form | spell | sound | freq | english | esperanto | french | german | icelandic | russian | swahili | size | language | 1gram | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2147 | leising | 1 | 1 | 1.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | english | [l, e, i, s, i, n, g] |
| 726 | chat | 1 | 0 | 1.0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 4 | french | [c, h, a, t] |
| 906 | магнит | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 6 | russian | [м, а, г, н, и, т] |
| 815 | cloche | 1 | 0 | 1.0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 6 | french | [c, l, o, c, h, e] |
| 971 | né | 1 | 0 | 1.0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | french | [n, é] |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4209 | woman | 1 | 1 | 1.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | english | [w, o, m, a, n] |
| 661 | vifaa | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | swahili | [v, i, f, a, a] |
| 398 | среди | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 5 | russian | [с, р, е, д, и] |
| 261 | kuhusu | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 6 | swahili | [k, u, h, u, s, u] |
| 593 | si | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | swahili | [s, i] |
4251 rows × 14 columns
In [20]:
## 2gram列の追加
bigrams = ngrams.gen_bigrams(bases, sep = r"", check = False)
## 包括的 2gram の作成
if ngram_is_inclusive:
bigrams = [ [*b, *u] for b, u in zip(bigrams, unigrams) ]
if verbose:
print(random.sample(bigrams, 3))
In [21]:
df['2gram'] = bigrams
if verbose:
df
/var/folders/s2/lk8hdt6j10j0xyycw1lbjsm40000gn/T/ipykernel_2920/1480305306.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy df['2gram'] = bigrams
In [22]:
## 3gram列の追加
trigrams = ngrams.gen_trigrams(bases, sep = r"", check = False)
## 包括的 3gram の作成
if ngram_is_inclusive:
trigrams = [ [ *t, *b ] for t, b in zip(trigrams, bigrams) ]
if verbose:
print(random.sample(trigrams, 3))
In [23]:
df['3gram'] = trigrams
if verbose:
df
/var/folders/s2/lk8hdt6j10j0xyycw1lbjsm40000gn/T/ipykernel_2920/3715201492.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy df['3gram'] = trigrams
In [24]:
## skippy 2grams の生成
import sys
sys.path.append("..") # library path に一つ上の階層を追加
import ngrams_skippy
skippy_2grams = [ ngrams_skippy.generate_skippy_bigrams(x,
missing_mark = '…',
max_distance = max_distance_val, check = False)
for x in df['1gram'] ]
## 包括的 skippy 2-grams の生成
if ngram_is_inclusive:
for i, b2 in enumerate(skippy_2grams):
b2.extend(unigrams[i])
#
if verbose:
random.sample(skippy_2grams, 3)
In [25]:
## skippy 2gram 列の追加
df['skippy2gram'] = skippy_2grams
df
/var/folders/s2/lk8hdt6j10j0xyycw1lbjsm40000gn/T/ipykernel_2920/3263801935.py:3: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy df['skippy2gram'] = skippy_2grams
Out[25]:
| form | spell | sound | freq | english | esperanto | french | german | icelandic | russian | swahili | size | language | 1gram | 2gram | 3gram | skippy2gram | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2147 | leising | 1 | 1 | 1.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | english | [l, e, i, s, i, n, g] | [le, ei, is, si, in, ng, l, e, i, s, i, n, g] | [lei, eis, isi, sin, ing, le, ei, is, si, in, ... | [le, l…i, l…s, l…n, l…g, ei, e…s, e…i, e…n, e…... |
| 726 | chat | 1 | 0 | 1.0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 4 | french | [c, h, a, t] | [ch, ha, at, c, h, a, t] | [cha, hat, ch, ha, at, c, h, a, t] | [ch, c…a, c…t, ha, h…t, at, c, h, a, t] |
| 906 | магнит | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 6 | russian | [м, а, г, н, и, т] | [ма, аг, гн, ни, ит, м, а, г, н, и, т] | [маг, агн, гни, нит, ма, аг, гн, ни, ит, м, а,... | [ма, м…г, м…н, м…и, м…т, аг, а…н, а…и, а…т, гн... |
| 815 | cloche | 1 | 0 | 1.0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 6 | french | [c, l, o, c, h, e] | [cl, lo, oc, ch, he, c, l, o, c, h, e] | [clo, loc, och, che, cl, lo, oc, ch, he, c, l,... | [cl, c…o, c…c, c…h, c…e, lo, l…c, l…h, l…e, oc... |
| 971 | né | 1 | 0 | 1.0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | french | [n, é] | [né, n, é] | [n, é, né, n, é] | [né, n, é] |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4209 | woman | 1 | 1 | 1.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | english | [w, o, m, a, n] | [wo, om, ma, an, w, o, m, a, n] | [wom, oma, man, wo, om, ma, an, w, o, m, a, n] | [wo, w…m, w…a, w…n, om, o…a, o…n, ma, m…n, an,... |
| 661 | vifaa | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | swahili | [v, i, f, a, a] | [vi, if, fa, aa, v, i, f, a, a] | [vif, ifa, faa, vi, if, fa, aa, v, i, f, a, a] | [vi, v…f, v…a, if, i…a, fa, f…a, aa, v, i, f, ... |
| 398 | среди | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 5 | russian | [с, р, е, д, и] | [ср, ре, ед, ди, с, р, е, д, и] | [сре, ред, еди, ср, ре, ед, ди, с, р, е, д, и] | [ср, с…е, с…д, с…и, ре, р…д, р…и, ед, е…и, ди,... |
| 261 | kuhusu | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 6 | swahili | [k, u, h, u, s, u] | [ku, uh, hu, us, su, k, u, h, u, s, u] | [kuh, uhu, hus, usu, ku, uh, hu, us, su, k, u,... | [ku, k…h, k…u, k…s, uh, u…u, u…s, hu, h…s, h…u... |
| 593 | si | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | swahili | [s, i] | [si, s, i] | [s, i, si, s, i] | [si, s, i] |
4251 rows × 17 columns
In [26]:
## skippy 3grams の生成
import sys
sys.path.append("..") # library path に一つ上の階層を追加
import ngrams_skippy
skippy_3grams = [ ngrams_skippy.generate_skippy_trigrams(x,
missing_mark = '…',
max_distance = max_distance_val, check = False)
for x in df['1gram'] ]
## 包括的 skippy 3-grams の生成
if ngram_is_inclusive:
for i, t2 in enumerate(skippy_3grams):
t2.extend(skippy_2grams[i])
#
if verbose:
random.sample(skippy_3grams, 3)
In [27]:
## skippy 3gram 列の追加
df['skippy3gram'] = skippy_3grams
df
/var/folders/s2/lk8hdt6j10j0xyycw1lbjsm40000gn/T/ipykernel_2920/1159231133.py:3: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy df['skippy3gram'] = skippy_3grams
Out[27]:
| form | spell | sound | freq | english | esperanto | french | german | icelandic | russian | swahili | size | language | 1gram | 2gram | 3gram | skippy2gram | skippy3gram | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2147 | leising | 1 | 1 | 1.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | english | [l, e, i, s, i, n, g] | [le, ei, is, si, in, ng, l, e, i, s, i, n, g] | [lei, eis, isi, sin, ing, le, ei, is, si, in, ... | [le, l…i, l…s, l…n, l…g, ei, e…s, e…i, e…n, e…... | [lei, le…s, le…i, le…n, le…g, l…is, l…i…i, l…i... |
| 726 | chat | 1 | 0 | 1.0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 4 | french | [c, h, a, t] | [ch, ha, at, c, h, a, t] | [cha, hat, ch, ha, at, c, h, a, t] | [ch, c…a, c…t, ha, h…t, at, c, h, a, t] | [cha, ch…t, c…at, hat, ch, c…a, c…t, ha, h…t, ... |
| 906 | магнит | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 6 | russian | [м, а, г, н, и, т] | [ма, аг, гн, ни, ит, м, а, г, н, и, т] | [маг, агн, гни, нит, ма, аг, гн, ни, ит, м, а,... | [ма, м…г, м…н, м…и, м…т, аг, а…н, а…и, а…т, гн... | [маг, ма…н, ма…и, ма…т, м…гн, м…г…и, м…г…т, м…... |
| 815 | cloche | 1 | 0 | 1.0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 6 | french | [c, l, o, c, h, e] | [cl, lo, oc, ch, he, c, l, o, c, h, e] | [clo, loc, och, che, cl, lo, oc, ch, he, c, l,... | [cl, c…o, c…c, c…h, c…e, lo, l…c, l…h, l…e, oc... | [clo, cl…c, cl…h, cl…e, c…oc, c…o…h, c…o…e, c…... |
| 971 | né | 1 | 0 | 1.0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | french | [n, é] | [né, n, é] | [n, é, né, n, é] | [né, n, é] | [né, né, n, é] |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4209 | woman | 1 | 1 | 1.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | english | [w, o, m, a, n] | [wo, om, ma, an, w, o, m, a, n] | [wom, oma, man, wo, om, ma, an, w, o, m, a, n] | [wo, w…m, w…a, w…n, om, o…a, o…n, ma, m…n, an,... | [wom, wo…a, wo…n, w…ma, w…m…n, w…an, oma, om…n... |
| 661 | vifaa | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | swahili | [v, i, f, a, a] | [vi, if, fa, aa, v, i, f, a, a] | [vif, ifa, faa, vi, if, fa, aa, v, i, f, a, a] | [vi, v…f, v…a, if, i…a, fa, f…a, aa, v, i, f, ... | [vif, vi…a, v…fa, v…f…a, v…aa, ifa, if…a, i…aa... |
| 398 | среди | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 5 | russian | [с, р, е, д, и] | [ср, ре, ед, ди, с, р, е, д, и] | [сре, ред, еди, ср, ре, ед, ди, с, р, е, д, и] | [ср, с…е, с…д, с…и, ре, р…д, р…и, ед, е…и, ди,... | [сре, ср…д, ср…и, с…ед, с…е…и, с…ди, ред, ре…и... |
| 261 | kuhusu | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 6 | swahili | [k, u, h, u, s, u] | [ku, uh, hu, us, su, k, u, h, u, s, u] | [kuh, uhu, hus, usu, ku, uh, hu, us, su, k, u,... | [ku, k…h, k…u, k…s, uh, u…u, u…s, hu, h…s, h…u... | [kuh, ku…u, ku…s, k…hu, k…h…s, k…h…u, k…us, k…... |
| 593 | si | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | swahili | [s, i] | [si, s, i] | [s, i, si, s, i] | [si, s, i] | [si, si, s, i] |
4251 rows × 18 columns
In [28]:
## LDA 構築の基になる document-term matrix (dtm) を構築
from gensim.corpora.dictionary import Dictionary
bots = df[term_type]
diction = Dictionary(bots)
## 結果の確認
print(diction)
Dictionary<36865 unique tokens: ['e', 'ei', 'eis', 'ei…g', 'ei…i']...>
In [29]:
## diction の濾過
import copy
diction_copy = copy.deepcopy(diction)
## filter適用: 実は諸刃の刃で,token数が少ない時には適用しない方が良い
print(f"min freq filter: {term_min_freq}")
print(f"abuse filter: {term_abuse_threshold}")
apply_filter = True
if apply_filter:
diction_copy.filter_extremes(no_below = term_min_freq, no_above = term_abuse_threshold)
## check
print(diction_copy)
min freq filter: 2 abuse filter: 0.05 Dictionary<20626 unique tokens: ['ei', 'eis', 'ei…g', 'ei…i', 'ei…n']...>
In [30]:
## Corpus (gensim の用語では corpus) の構築
corpus = [ diction.doc2bow(bot) for bot in bots ]
## check
check = True
if verbose:
sample_n = 5
print(random.sample(corpus, sample_n))
#
print(f"Number of documents: {len(corpus)}")
Number of documents: 4251
In [31]:
## LDA モデルの構築
from gensim.models import LdaModel
#from tqdm import tqdm
## LDAモデル
print(f"Building LDA model with n_topics: {n_topics}")
lda = LdaModel(corpus, id2word = diction, num_topics = n_topics, alpha = 0.01)
#
print(lda) # print(..)しないと中身が見れない
Building LDA model with n_topics: 5 LdaModel<num_terms=36865, num_topics=5, decay=0.5, chunksize=2000>
In [32]:
%%capture --no-display
## LDA のtopic ごとに,関連度の高い term を表示
import pandas as pd
n_terms = 20 # topic ごとに表示する term 数の指定
topic_dfs = [ ]
for topic in range(n_topics):
terms = [ ]
for i, prob in lda.get_topic_terms(topic, topn = n_terms):
terms.append(diction.id2token[ int(i) ])
#
topic_dfs.append(pd.DataFrame([terms], index = [ f'topic {topic+1}' ]))
#
topic_term_df = pd.concat(topic_dfs)
## Table で表示
topic_term_df.T
Out[32]:
| topic 1 | topic 2 | topic 3 | topic 4 | topic 5 | |
|---|---|---|---|---|---|
| 0 | a | i | о | e | e |
| 1 | m | u | а | r | r |
| 2 | e | a | т | o | t |
| 3 | r | k | и | i | i |
| 4 | s | n | р | n | d |
| 5 | u | s | с | t | е |
| 6 | a…a | g | е | s | i…e |
| 7 | i | ku | в | l | e…e |
| 8 | a…e | h | н | a | с |
| 9 | t | a…i | ь | u | n |
| 10 | k | l | д | c | r…e |
| 11 | b | k…a | л | h | t…e |
| 12 | h | е | e | p | g |
| 13 | a…r | e | о…ь | r…e | a |
| 14 | ar | d | о…т | o…e | в |
| 15 | a…i | si | i | e…e | re |
| 16 | m…a | о | к | m | e…r |
| 17 | d | u…i | ть | i…e | о |
| 18 | l | u…a | п | g | er |
| 19 | ma | k…i | о…а | er | e…i |
In [33]:
%%capture --no-display
## pyLDAvis を使った結果 LDA の可視化: 階層クラスタリングより詳しい
import pyLDAvis
#installed_version = sys.version
installed_version = pyLDAvis.__version__
print(f"installed_version: {installed_version}")
if float(installed_version[:3]) > 3.1:
import pyLDAvis.gensim_models as gensimvis
else:
import pyLDAvis.gensim as gensimvis
#
pyLDAvis.enable_notebook()
#
lda_used = lda
corpus_used = corpus
diction_used = diction
## 実行パラメター
use_tSNE = False
if use_tSNE:
vis = gensimvis.prepare(lda_used, corpus_used, diction_used, mds = 'tsne',
n_jobs = 1, sort_topics = False)
else:
vis = gensimvis.prepare(lda_used, corpus_used, diction_used,
n_jobs = 1, sort_topics = False)
#
pyLDAvis.display(vis)
## 結果について
## topic を表わす円の重なりが多いならn_topics が多過ぎる可能性がある.
## ただし2Dで重なっていても,3Dなら重なっていない可能性もある
Out[33]:
In [34]:
## LDA がD に対して生成した topics の弁別性を確認
## 得られたtopics を確認
topic_dist = lda.get_topics()
if verbose:
topic_dist
In [35]:
## 検査 1: topic ごとに分布の和を取る
print(topic_dist.sum(axis = 1))
[1.0000001 1.0000001 1. 1. 0.99999994]
In [36]:
## 検査 2: 総和を求める: n_topics にほぼ等しいなら正常
print(topic_dist.sum())
5.0
In [37]:
## term エンコード値の分布を確認
import matplotlib.pyplot as plt
plt.figure(figsize = (4,5))
sampling_rate = 0.3
df_size = len(topic_dist)
sample_n = round(df_size * sampling_rate)
topic_sampled = random.sample(list(topic_dist), sample_n)
T = sorted([ sorted(x, reverse = True) for x in topic_sampled ])
plt.plot(T, range(len(T)))
plt.title("Distribution of sorted values ({sample_n} samples) for topic/term encoding")
plt.show()
In [38]:
## tSNE を使った topics のグループ化 (3D)
from sklearn.manifold import TSNE
import numpy as np
## tSNE のパラメターを設定
## n_components は射影先の空間の次元: n_components = 3 なら3次元空間に射影
## perplexity は結合の強さを表わす指数で,値に拠って結果が代わるので,色々な値を試すと良い
#perplexity_val = 10 # 大き過ぎると良くない
top_perplexity_reduct_rate = 0.3
perplexity_val = round(len(topic_dist) * top_perplexity_reduct_rate)
topic_tSNE_3d = TSNE(n_components = 3, random_state = 0, perplexity = perplexity_val, n_iter = 1000)
## データに適用
top_tSNE_3d_fitted = topic_tSNE_3d.fit_transform(np.array(topic_dist))
In [39]:
## Plotlyを使って tSNE の結果の可視化 (3D)
#import plotly.express as pex
import plotly.graph_objects as go
import numpy as np
top_tSNE = top_tSNE_3d_fitted
fig = go.Figure(data = [go.Scatter3d(x = top_tSNE[:,0], y = top_tSNE[:,1], z = top_tSNE[:,2],
mode = 'markers')])
## 3D 散布図にラベルを追加する処理は未実装
title_val = f"3D tSNE view for LDA (#topics: {n_topics}, doc: {doc_type}, term: {term_size} {term_type})"
fig.update_layout(autosize = False,
width = 600, height = 600, title = title_val)
fig.show()
In [40]:
## 構築した LDA モデルを使って文(書)を分類する
## .get_document_topics(..) は minimu_probability = 0としないと
## topic の値が小さい場合に値を返さないので,
## パラメター
ntopics = n_topics # LDA の構築の最に指定した値を使う
check = False
encoding = [ ]
for i, row in df.iterrows():
if check:
print(f"row: {row}")
doc = row[doc_type]
bot = row[term_type]
## get_document_topics(..) では minimu_probability = 0 としないと
## 値が十分に大きな topics に関してだけ値が取れる
enc = lda.get_document_topics(diction.doc2bow(bot), minimum_probability = 0)
if check:
print(f"enc: {enc}")
encoding.append(enc)
#
len(encoding)
Out[40]:
4251
In [41]:
## enc 列の追加
#df['enc'] = np.array(encoding) # This flattens arrays
#df['enc'] = list(encoding) # ineffective
df['enc'] = [ list(map(lambda x: x[1], y)) for y in encoding ]
if verbose:
df['enc']
/var/folders/s2/lk8hdt6j10j0xyycw1lbjsm40000gn/T/ipykernel_2920/1047258704.py:5: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
In [42]:
## エンコーディングのstd の分布を見る
from scipy.stats import tstd
from matplotlib import pyplot as plt
plt.figure(figsize = (6,4))
std_data = [ tstd(x) for x in df['enc'] ]
plt.hist(std_data)
plt.title("Distribution of standard deviations")
plt.show()
In [43]:
## doc のエンコーディング
## 一様分布の事例を除外
from scipy.stats import tstd # standard deviation の計算用
print(f"{len(df)} instances before filtering")
check = False
doc_enc = df['enc']
max_std = max([ tstd(x) for x in doc_enc])
if check: print(f"std max: {max_std}")
min_std = min([ tstd(x) for x in doc_enc])
if check: print(f"std min: {min_std}")
first_min_std = list(sorted(set([ tstd(x) for x in doc_enc])))[-0]
print(f"std 1st min: {first_min_std}")
second_min_std = list(sorted(set([ tstd(x) for x in doc_enc])))[-1]
print(f"std 2nd min: {second_min_std}")
4251 instances before filtering std 1st min: 0.1331463676125301 std 2nd min: 0.4471107020924255
In [44]:
## df_filtered の定義
## 閾値は2番目に小さい値より小さく最小値よりは大きな値であるべき
std_threshold = second_min_std / 4 # 穏健な値を得るために4で割った
print(f"std_threshold: {std_threshold}")
## Rっぽい次のコードは通らない
#df_filtered = df[ df['encoding'] > std_threshold ]
## 通るのは次のコード: Creating a list of True/False and apply it to DataFrame
std_tested = [ False if tstd(x) < std_threshold else True for x in df['enc'] ]
df_filtered = df[ std_tested ]
#
print(f"{len(df_filtered)} instances after filtering ({len(df) - len(df_filtered)} instances removed)")
std_threshold: 0.11177767552310637 4251 instances after filtering (0 instances removed)
In [45]:
## doc エンコード値の分布を確認
sample_n = 50
E = sorted([ sorted(x, reverse = True) for x in df_filtered['enc'].sample(sample_n) ])
plt.figure(figsize = (5,5))
plt.plot(E, range(len(E)))
plt.title(f"Distribution of sorted encoding values for sampled {sample_n} docs")
plt.show()
In [46]:
len(df_filtered['language'])
Out[46]:
4251
In [47]:
## tSNE 用の事例サンプリング = tSNE_df の定義
tSNE_sampling = True
tSNE_sampling_rate = 0.33
if tSNE_sampling:
tSNE_df_original = df_filtered.copy()
sample_n = round(len(tSNE_df_original) * tSNE_sampling_rate)
tSNE_df = tSNE_df_original.sample(sample_n)
print(f"tSNE_df has {len(tSNE_df)} rows after sampling")
else:
tSNE_df = df_filtered
tSNE_df has 1403 rows after sampling
In [48]:
tSNE_df.columns
Out[48]:
Index(['form', 'spell', 'sound', 'freq', 'english', 'esperanto', 'french',
'german', 'icelandic', 'russian', 'swahili', 'size', 'language',
'1gram', '2gram', '3gram', 'skippy2gram', 'skippy3gram', 'enc'],
dtype='object')
In [49]:
## tSNE の結果の可視化: Plotly を使った 3D 描画
import numpy as np
from sklearn.manifold import TSNE as tSNE
import plotly.express as pex
import plotly.graph_objects as go
import matplotlib.pyplot as plt
## tSNE のパラメターを設定
perplexity_max_val = round(len(tSNE_df)/4)
for perplexity_val in range(5, perplexity_max_val, 30):
## tSNE 事例の生成
tSNE_3d_varied = tSNE(n_components = 3, random_state = 0, perplexity = perplexity_val, n_iter = 1000)
## データに適用
doc_enc = np.array(list(tSNE_df['enc']))
doc_tSNE_3d_varied = tSNE_3d_varied.fit_transform(doc_enc)
T = zip(doc_tSNE_3d_varied[:,0], doc_tSNE_3d_varied[:,1], doc_tSNE_3d_varied[:,2],
tSNE_df['language']) # zip(..)が必要
df = pd.DataFrame(T, columns = ['D1', 'D2', 'D3', 'language'])
## 作図
fig = go.Figure()
for lang in np.unique(df['language']):
part = df[df['language'] == lang]
fig.add_trace(
go.Scatter3d(
x = part['D1'], y = part['D2'], z = part['D3'],
name = lang, mode = 'markers', marker = dict(size = 6),
showlegend = True
)
)
title_val = f"tSNE 3D map (ppl: {perplexity_val}) of {doc_attr}s encoded by LDA ({n_topics} topics; term: {term_type})"
fig.update_layout(title = dict(text = title_val),
autosize = False, width = 600, height = 600,)
fig.show()
In [50]:
## 階層クラスタリングのための事例のサンプリング
hc_sampling_rate = 0.1 # 大きくし過ぎると図が見にくい
df_size = len(tSNE_df)
hc_sample_n = round(df_size * hc_sampling_rate)
hc_df = tSNE_df.sample(hc_sample_n)
##
print(f"{hc_sample_n} rows are sampled")
hc_df['language'].value_counts()
140 rows are sampled
Out[50]:
language french 38 english 30 russian 28 swahili 22 german 22 Name: count, dtype: int64
In [51]:
## doc 階層クラスタリングの実行
import numpy as np
import plotly
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage
## 距離行列の生成
Enc = list(hc_df['enc'])
linkage = linkage(Enc, method = 'ward', metric = 'euclidean')
## 描画サイズの指定
plt.figure(figsize = (5, round(len(hc_df) * 0.15))) # This needs to be run here, before dendrogram construction.
## 事例ラベルの生成
label_vals = [ x[:max_doc_size] for x in list(hc_df[doc_type]) ] # truncate doc keys
## 樹状分岐図の作成
dendrogram(linkage, orientation = 'left', labels = label_vals, leaf_font_size = 7)
## 描画
plt.title(f"Hierarchical clustering of (sampled) {len(hc_df)} (= {100 * hc_sampling_rate}%) {doc_attr}s as docs\n \
encoded via LDA ({n_topics} topics) with {term_type} as terms")
## ラベルに language に対応する色を付ける
lang_colors = { lang_name : i for i, lang_name in enumerate(np.unique(hc_df['language'])) }
ax = plt.gca()
for ticker in ax.get_ymajorticklabels():
form = ticker.get_text()
row = hc_df.loc[hc_df[doc_type] == form]
#lang = row['language']
lang = row['language'].to_string().split()[-1] # trick
try:
lang_id = lang_colors[lang]
except (TypeError, KeyError):
print(f"color encoding error at: {lang}")
#
ticker.set_color(plotly.colors.qualitative.Plotly[lang_id]) # id の基数調整
#
plt.show()
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## tSNE の結果の可視化 (2D)
#import seaborn as sns
import numpy as np
import plotly
import plotly.express as pex
import matplotlib.pyplot as plt
from adjustText import adjust_text
## tSNE 事例の生成
perplexity_selected = 250
tSNE_3d = tSNE(n_components = 3, random_state = 0, perplexity = perplexity_selected, n_iter = 1000)
## データに適用
doc_enc = np.array(list(tSNE_df['enc']))
doc_tSNE_3d = tSNE_3d.fit_transform(doc_enc)
T = zip(doc_tSNE_3d[:,0], doc_tSNE_3d[:,1], doc_tSNE_3d[:,2],
tSNE_df['language']) # zip(..)が必要
df = pd.DataFrame(T, columns = ['D1', 'D2', 'D3', 'language'])
## 描画
plt.figure(figsize = (5, 5))
plt.set_colors = pex.colors.qualitative.Plotly
for r in [ np.roll([0,1,2], -i) for i in range(0,3) ]:
if check:
print(r)
X, Y = df.iloc[:,r[0]], df.iloc[:,r[1]]
gmax = max(X.max(), Y.max())
gmin = min(X.min(), Y.min())
plt.xlim(gmin, gmax)
plt.ylim(gmin, gmax)
colormap = pex.colors.qualitative.Plotly
lang_list = list(np.unique(tSNE_df['language']))
cmapped = [ colormap[lang_list.index(lang)] for lang in df['language'] ]
scatter = plt.scatter(X, Y, s = 40, c = cmapped, edgecolors = 'w')
## 文字を表示する事例のサンプリング
lab_sampling_rate = 0.02
lab_sample_n = round(len(tSNE_df) * lab_sampling_rate)
sampled_keys = [ doc[:max_doc_size] for doc in random.sample(list(tSNE_df[doc_type]), lab_sample_n) ]
## labels の生成
texts = [ ]
for x, y, s in zip(X, Y, sampled_keys):
texts.append(plt.text(x, y, s, size = 9, color = 'blue'))
## label に repel を追加: adjustText package の導入が必要
adjust_text(texts, force_points = 0.2, force_text = 0.2,
expand_points = (1, 1), expand_text = (1, 1),
arrowprops = dict(arrowstyle = "-", color = 'black', lw = 0.5))
#
plt.title(f"tSNE (ppl: {perplexity_selected}) 2D map of {len(tSNE_df)} {doc_attr}s via LDA ({term_type}; {n_topics} topics)")
#plt.legend(np.unique(cmapped))
plt.show()
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